Quality control for the geophone reorientation of ocean bottom seismic data using k‐means clustering

Abstract

During ocean bottom seismic acquisition, seafloor multicomponent geophones located in rugose and sloping water bottom can be affected by skewed energy distribution, such as leaked shear energy on the vertical geophones and leaked compressional energy on the horizontal geophones. To correct for the tilted energy distribution, which is one of most effective preprocessing steps, a geophone reorientation step is applied. This is a simple and straightforward process that applies a 3‐dimensional rotation matrix with respect to the orientation angles. Since the reorientation process highly affects the outcome of the entire data processing workflow, it has to be accompanied by a careful quality control process to verify its validity for the whole survey area. In this study, we propose a quality control workflow for the geophone reorientation by using unsupervised machine learning. A correlation analysis is employed to compare numerical versus analytical solutions of both the azimuth and the incidence angles for the direct arrivals. A comparison of both solutions aims to generate correlation coefficients that are indicative of the accuracy of geophone orientation. The correlation coefficients are subsequently investigated by the k‐means clustering algorithm to differentiate and identify normally and abnormally deployed/reoriented geophones. Numerical experiments on a field ocean bottom seismic data set confirm that the proposed workflow effectively provides reliable labels for normally and abnormally deployed/reoriented geophones. The labelling assigned by the proposed quality control workflow is a suitable indicator for abnormalities in the geophone reorientation step and will be helpful for further investigation, such as re‐correction or removal of abnormally reoriented geophones.

Wavefield Decomposition of Ocean-Bottom Multicomponent Seismic Data with Composite Calibration Filters

“…Before the first stage of decomposition, multicomponent OBS data must undergo preprocessing, including OBS relocation [36] and OBS orientation [37,38]. The raw data were severely affected by noise, especially the water column-related low-frequency noises in the pressure data recorded by hydrophones.…”

High-precision matching algorithm for multi-image segmentation of micro animation videos in mobile network environment

“…K-means clustering analysis algorithm has the advantages of simple, easy to understand, strong adaptability and efficient clustering [18][19][20], and it is a widely used clustering analysis algorithm at present. According to the known Euclidean distance between the SIFT feature vectors of the key points of the micro-animation video sub-images, the K-means clustering analysis algorithm is used to cluster these feature vectors.…”